LinkedMDR: A Collective Knowledge Representation of a Heterogeneous Document Corpus - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2017

LinkedMDR: A Collective Knowledge Representation of a Heterogeneous Document Corpus

Résumé

The ever increasing need for extracting knowledge from heterogeneous data has become a major concern. This is particularly observed in many application domains where several actors, with different expertise, exchange a great amount of information at any stage of a large-scale project. In this paper, we propose LinkedMDR: a novel ontology for Linked Multimedia Document Representation that describes the knowledge of a heterogeneous document corpus in a semantic data network. LinkedMDR combines existing standards and introduces new components that handle the connections between these standards and augment their capabilities. It is generic and offers a pluggable layer that makes it adaptable to different domain-specific knowledge. Experiments conducted on construction projects show that LinkedMDR is applicable in real-world scenarios.
Fichier principal
Vignette du fichier
DEXA2017_Paper11559_FinalVersion.pdf (660.72 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01817333 , version 1 (26-03-2019)

Identifiants

Citer

Nathalie Charbel, Christian Sallaberry, Sébastien Laborie, Gilbert Tekli, Richard Chbeir. LinkedMDR: A Collective Knowledge Representation of a Heterogeneous Document Corpus. The 28th International Conference on Database and Expert Systems Applications (DEXA 2017), Aug 2017, Lyon, France. pp.362-377, ⟨10.1007/978-3-319-64468-4_28⟩. ⟨hal-01817333⟩

Collections

UNIV-PAU LIUPPA
94 Consultations
160 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More